Update app.py

app.py

@@ -13,6 +13,7 @@ from pathlib import Path
 import random
 import PIL
 
+#Primero definimos todas las funciones, clases y variables que sopn necesarias para que esto funcione
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 def transform_image(image):
     my_transforms = transforms.Compose([transforms.ToTensor(),
@@ -70,6 +71,7 @@ class SegmentationAlbumentationsTransform(ItemTransform):
         aug = self.aug(image=np.array(img), mask=np.array(mask))
         return PILImage.create(aug["image"]), PILMask.create(aug["mask"])
 
+#Cargamos el modelo
 repo_id = "luisvarona/Practica3"
 learn = from_pretrained_fastai(repo_id)
 model = learn.model
@@ -99,4 +101,4 @@ def predict(img):
     # return {labels[i]: float(probs[i]) for i in range(len(labels))}
     
 # Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.inputs.Image(shape=(649, 480)),examples=['color_154.jpg','color_154 (1).jpg']).launch(share=False)
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.inputs.Image(shape=(649, 480)),examples=['color_155.jpg','color_154 (1).jpg']).launch(share=False)